5 Acknowledgements We thank the Kentucky Department for Energy Development and Independence for funding for this project. We also thank Aron Patrick at the Department for providing much of the data used in this report as well as answering questions about the data. We thank Paul Brooks, Talina Matthews, Aron Patrick, and Alan Waddell for useful comments. However, all errors are solely the responsibility of the authors. iv

6 Executive Summary There is growing concern over the emissions of greenhouse gases in the United States. Policymakers at both the state and national levels have discussed, and in some cases enacted, policies with the goals of reducing energy demand and encouraging the use of more efficient energy technologies. Because these policies will have an effect on the cost of energy, a quantitative examination of the energy demand is warranted. In this project, we estimate the likely effects of increased electricity prices on the demand for electricity, production as measured by Gross State Product (GSP), and employment. We estimate two sets of models. In the first set, we study the relationship between electricity consumption and the prices for energy sources including electricity. These models are known as demand equations. In the second set, we build on the demand equations and estimate the relationship between economic output (measured either as gross domestic product or employment) and energy prices. The goal of these models is to estimate the impact of energy prices on economic output in both the short run and the long run. We highlight this distinction to illustrate that the response to a change in energy prices may be different in the short run because some factors may be difficult to modify over a short period. For example, the process of converting existing power plants to use natural gas instead of coal is a time-consuming process that does not happen overnight. In addition to presenting the results from our economic models, we also provide policy scenarios that illustrate the long-run effects of a permanent increase in electricity prices of either 10% or 25%. We assume that the price shock is the only change of note. Other important factors such as changes in energy efficiency or costs of production are not considered because the likely changes in such factors are difficult if not impossible to predict. Furthermore, an economic model that allows for such changes is much more difficult to estimate than our economic model. Even with these caveats, the policy scenarios simulate future economic conditions under current policies and conditions. They answer the question of what would happen to future GSP and employment if electricity prices went up and nothing else changed. The major findings of the report are: In the short run, coal appears to be the most sensitive to its price changes followed by natural gas consumption. For example, a 1% increase in price results in a 0.64% drop in coal consumption and a 0.42% drop in natural gas consumption. For electricity, a 1% percent increase in its price results in a 0.20% drop in electricity consumption, which is substantially below both coal and natural gas. In addition, a 1% increase in income leads to an increase in electricity consumption and fuel oil consumption of 0.13% and 0.43%, respectively. As expected, consumers are more sensitive to long-run price increases rather than short-run price increases. In the long run, a 1% increase in price results in a 1

7 1.71% and 1.31% drop in consumption of natural gas and coal, respectively. For electricity, a 1% percent increase in its price results in a 0.72% drop in electricity consumption. When we look at the demand for electricity across different sectors of the economy, we find that the industrial sector is the most sensitive to price changes in both the short and long run. The residential sector is less sensitive to price changes, and is sensitive to price changes only in the short run. The results suggest that the industrial and commercial sectors are the quickest to alter their electricity consumption, which could negatively affect economic growth and employment. Energy prices have the expected negative relationship with GSP growth and GSP levels. However, crude oil prices appear to have more of an effect on production growth compared to electricity prices and natural gas prices. Similarly, energy prices have a negative relationship with employment growth and employment levels. The effect of energy prices on employment is similar for electricity, natural gas, and crude oil. We illustrate our findings through a set of policy scenarios of assumed 10% and 25% increases in electricity prices for energy-intensive states such as Kentucky. We consider both short-run and long-effects of these price increases. For each scenario, we assume that the price increase is permanent but is not accompanied by any other notable changes such as technological advancement or the discovery of new energy supplies. We assume that, in the absence of the price shock, economic growth consists of 3% annual growth in GSP and 1% annual growth in employment, the historical averages for each. A 25% electricity price increase is estimated to reduce the GSP growth rate from 3% to 2.30% in the long run. The price increase is estimated to reduce employment growth from 1% to 0.61% in the long run. 2

8 Introduction There is growing concern over the emissions of greenhouse gases in the United States. Policymakers at both the state and national levels have discussed, and in some cases enacted, policies with the goals of reducing energy demand and encouraging the use of more efficient energy technologies. Because these policies will have an effect on the cost of energy, a quantitative examination of the energy demand is warranted. In this project, we estimate the likely effects of increased electricity prices on the demand for electricity, production as measured by Gross State Product (GSP), and employment. We begin by discussing the strengths and weakness of various economic models used to estimate these relationships. We focus on the two main types of models used to examine long-run relationships among energy and economic activity include computable general equilibrium models (CGE) and capital, labor, energy and materials models (CLEM). Then, we describe trends over time in electricity and other energy prices, electricity consumption, U.S. Gross Domestic Product (GDP), and employment. The descriptive analysis of the trends in energy prices, electricity consumption, and economic conditions provide insight into the underlying relationships among these variables of interest. In the next section we document the data and build a simple stock-flow model of energy demand. We look at overall demand for electricity, coal, natural gas, and fuel oil. For electricity demand, we look at overall demand as well as demand by sector: commercial, residential, and industrial. These models provide estimates of both the short-run and long-run relationships between energy demanded and energy price, income, and substitute price. With these estimates in mind, we develop a model to investigate the dynamic relationship between energy prices and macroeconomic aggregates (i.e. employment and production) to ascertain the affects of changes in energy prices on these aggregates. Finally, we develop policy scenarios and corresponding estimates to produce predictions of the longrun effects of electricity price shocks on economic conditions under the strong assumption that the electricity price shock is not accompanied by other changes such as technological advances. 3

9 Literature Review In this section we briefly review the current literature of the relationship between energy and economic activity, namely, production and employment. The short-run model is relatively straight forward because the factors of production, except labor, are assumed to be fixed. The long-run model is more involved due to general equilibrium effects. Specifically, mobility of factors of production is a concern in the long run, but it is assumed to be constant in the short run. The two dominant models used to examine long-run relationships among energy and economic activity include computable general equilibrium models (CGE) and capital, labor, energy and materials models (CLEM). In what follows we analyze the relative strengths and weaknesses of the two models in order to pick the optimal one to use for examining the long-run relationship between energy and economic activity. Computable General Equilibrium (CGE) Model CGE models are a primary tool for analyzing policy changes over multiple markets. These models allow researchers to trace out the effects of a policy change (e.g. a change in tax rates, subsidies or regulations) that can be transmitted through multiple markets. This approach has been taken by many in examining such things as fiscal reform and development (see, e.g. Perry et al 2001; Gunning and Keyzer, 1995) and have become increasingly important in analyzing such things as environmental regulations (see, e.g. Weyant 1999; Bovenberg and Goulder 1996;Goulder 2002). Foundationally, CGE models are heavily rooted in economic theory. To begin, the system must be parameterized using elasticities of substitution between each pair of production inputs (and consumption goods) that come from the economic literature. Once the parameters are specified, the model is calibrated to reproduce the data for the benchmark year. This benchmark year represents the long-run equilibrium in the absence of a policy change. The system is then perturbed by a policy change (e.g. electricity price increase due to tax change) creating a new solution known as the counterfactual. For example, if the cap-and-trade system is the policy change, then the resulting equilibrium under the cap-and-trade system would be the counterfactual. The difference between the benchmark solution (i.e. the long-run equilibrium in absence of the policy change) and the counterfactual (i.e. the long-run equilibrium with the policy change) thus represents the effects of the policy change on the variables of interest (e.g. employment and production). Therefore, the main advantage of CGE models is their ability to measure the effects of policy changes over many markets in a theoretically-consistent manner. However, the usefulness of CGE models rests less on their predictive ability and more on illuminating the adjustment mechanism of prices and quantities in multiple markets (Wing, 2004). Consequently, as noted by Francois (2001), CGE models are an empirical tool used to analyze dynamic economic interactions given policy distortions. The main criticism of CGE models, as noted in Wing (2004), is that policy makers and researchers view these models as a black box, meaning that their results cannot be 4

10 meaningfully traced back to their data, parameters, solution method, or structure. The sheer complexity of such models makes it extremely difficult to pinpoint the precise source of the result (Panagariya and Duttagupta, 2001). A further difficulty rests on the accuracy of elasticities used for parameterization. The sometimes wide range of values for elasticity of substation found in the literature adds increased uncertainty about the final solutions. In other words, the model s results are likely dependent on the assumptions of the model, which include the elasticities of substitution among energy sources. Despite the criticisms of CGE models, they have been used extensively in examining energy policy. For instance, Li and Rose (1995) find the adverse effect of increased carbon taxes on Pennsylvania s economy were mitigated by substitution away from energy and towards other factors of production. Further, the adverse effects on industrial states were great for more mobile factors of production. Using data from Canada, Whalley and Trela (1986) examine interregional energy policies including a wide variety of energy taxes and subsidies on both producers and consumers, which could only be evaluated within a CGE framework. Capital, Labor, Energy and Materials (CLEM) Model CLEM models are models where energy prices are one of the main costs along with capital, labor, and materials (and sometimes other costs as well). Because the CLEM models are designed to evaluate effects of changes in energy prices, they are well suited for studying the effects of increased electricity prices as a result of policies aimed to capture environmental costs of carbon emissions. As opposed to CGE models, the actual structural parameters of the underlying model are not estimated, only their reduced-form counterpart. Rather than having to estimate specific theoretical relationships, this approach extracts information directly from the data or, in other words, allows the data to speak. The main advantage of reduced-form estimation relates to its ease in implementation and limited ex ante biases. The estimates of the reduced-form model provide the long-run multipliers associated with the underlying model. These estimators can then be used to estimate the variables of interest. Intuitively, if one is only interested in examining the statistical relationship among the variables of interest and not the parameters of the theoretical model, then this model is of particular interest (see, e.g., Kennedy, 2003). Many researchers have used CLEM type models to estimate the economic effects of oil price shocks in the 1970 s (see, e.g., Hamilton, 1983; Hooker, 1996; Davis and Haltiwanger, 2001). Davis and Haltiwanger (2001) examine the effects of oil and monetary shocks in the U.S. on job creation and destruction in the manufacturing sector from 1972 to They find that oil price shocks account for approximately twice as much variability in employment growth compared to monetary shocks and employment responses to oil price increases are exacerbated by capital intensity, energy intensity and product durability. 5

11 Comparison of Models With an interest in understanding the underlying relationship between energy and economic activity, it is important to limit the amount of prior assumptions and therefore any ex ante biases while attempting to extract as much information from the data as possible. There are many assumptions that go along with developing CGE models and subsequent simulations. For example, the mathematical structure of the production function and utility functions need to be developed. These assumptions, along with many other complexities associated with CGE models, limit the usefulness in describing the underlying relationship of interest. Because the CGE models require many assumptions on the structure of the economy and are very complex, we use a model implied by the CLEM type models for the long-run analysis. In particular, we use an autoregressive distributed lag (ARDL) model to limit our assumptions concerning the variables of interest and focus on the existing relationships extracted directly from the data. Given the comprehensiveness of the data along both the time and space dimension, this model will ease the examination of the causal relationship among the variables, thus allowing us to generating policy scenarios and corresponding outcomes. 6

12 Overview of Current Trends In this section we examine time trends of various variables of interest. We start by examining the trends in gross domestic product, employment, and crude oil prices. Figure 1 displays the trend in real crude oil prices. Expectedly, the 1970 s experienced rapid increases in crude oil prices coinciding with various factors. For instance, as documented by Hamilton (1993), in 1970 there was the rupture of the trans-arabian pipeline, Libyan production cutbacks, and coal price increases. From there was stagnating U.S. production and the OPEC embargo. Subsequently, from the Iranian revolution followed by the Iran-Iraq war and removal of U.S. price controls from The high oil prices encouraged production of oil from non-opec countries thus leading to a fall in oil prices until the Iraq s invasion of Kuwait in This invasion caused supplies to be cut back and prices to spike because Kuwait and Iraq accounted for 9% of total oil production at that time (Hamilton, 2010). Figure 1: Crude Oil Prices (2010$) Price per barrel $100 $90 $80 $70 $60 $50 $40 $30 $20 $10 $0 From 1997 to 2010 the new industrial age emerged as many countries began industrializing (e.g. Brazil, China, Hong Kong, India, Singapore, South Korea, Taiwan, and Thailand). These countries accounted for 17% of world s petroleum consumption in 1998 and accounted for 69% of the increase in global oil consumption as of 1998 (Hamilton, 2010). After some minor setbacks in the oil price due to the Asian financial crisis, oil prices continued on an upward trend as oil consumption continued and supply stagnated. 7

13 Figure 2 displays the time trend in real gross domestic product and total employment. As expected both series follow a steady upward trend with production leading employment. Following the oil supply shock of 1972, production declined sharply followed by employment. The large decline in production and employment in the early and late 1970 s coincide with rising energy prices and disruptions in petroleum supplies as documented previously. Figure 2: Gross Domestic Product (2010$) and Total Employment 160,000 Thousands of Workers 140, , ,000 80,000 60,000 40,000 20, ,000,000 14,000,000 12,000,000 10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 0 Millions of Dollars Employment Real GDP In order to get a sense of the relationship among the variables of interest for Kentucky, we compare Kentucky to Ohio, Tennessee and the United States as whole. These plots give us a sense of current trends over time against a backdrop of an ever-changing political, economical and demographical environment. 8

14 The following two graphs illustrate the energy intensity of each state. We use two measures of energy intensity: energy consumption per capita and energy consumption per GSP. Figure 3 shows the time series plot of total electricity consumption per capita. All series tend to follow similar upward trend over time with Kentucky surpassing Tennessee in the late 1980 s. Where Tennessee, Ohio and the U.S. have leveled off throughout the 1990 s, Kentucky has seen a large increase in electricity consumption per capita, likely a result of extremely low electricity prices when we document below. Figure 3: Electricity Consumption per Capita Billions of BTUs Kentucky Ohio Tennessee U.S. 9

15 Figure 4 presents electricity consumption normalized by state GSP. The figure shows that, in the mid to late 1980 s, Kentucky s electricity consumption per GSP exceeds Tennessee, in part because Tennessee s electricity consumption per GSP had been declining since the late 1970 s (see Figure 4). The figure indicates that Kentucky s electricity intensity is on the rise. Consequently, any increase in regulation or energy prices will bear a much larger burden in Kentucky relative to its neighboring states. Because electricity usage is normalized by GSP, these trends could be due to increases in production in Tennessee or, alternatively, a decrease in production in Kentucky. On the other hand, the United States and Ohio have seen relatively stable electricity intensity, at least when normalized by GSP. Figure 4: Electricity Consumption per Real Gross State Product Billions of BTUs Kentucky Ohio Tennessee U.S. 10

16 The time trend of average electricity price (in 2010 $), shown in Figure 5, illustrates Kentucky s superior position with the lowest average price compared to Ohio, Tennessee, and the United States for most of the time period under consideration. The general time trend follows historical patterns in regards to the energy crisis in the 1970 s, remaining relatively stable throughout the 1980 s and 1990 s, and proceeding to increase again in the beginning part of the 21 st century. Other prices, such as, fuel oil, natural gas, and coal, tend to follow national prices. Electricity price for Kentucky and Tennessee were very similar until the late 1980 s where they started to diverge. Figure 5: Electricity Price (2010$) Dollars per Million BTUs Kentucky Ohio Tennessee U.S. 11

17 Figure 6 displays time series plots of the five energy price variables along with the price of crude oil (prices are in 2010 dollars). From the graph, the price of coal has remained surprisingly stable over the time period. Likewise, the price of natural gas appears relatively stable; however, since the early 1970 s the price of natural gas has increased. This increase is perhaps due to the aforementioned oil price shocks of the early 1970 s. Furthermore, the price of fuel oil and motor gasoline appear to follow very similar time paths, thus appearing to be highly correlated. Overall, each price seems to be influenced, not surprisingly, by the price of oil. Figure 6: U.S. Energy Prices (2010$) Dollars per million BTUs Oil Electricity Coal Fuel Oil Natural Gas Motor Gasoline These cursory relationships provide insight into the underlying relationships among the variables of interest. We use these insights to develop more sophisticated econometric models in order to further understand the role of energy in the economy. Our goal is that these models allow us to take a closer look at the statistical relationships among the variables in both the short and long run. Specifically, the causal relationships among energy use, energy prices, employment and production are of particular interest. 12

18 Data and a Simple Energy Demand Model In this section we document the data and build a simple stock-flow model of energy demand with the intention of estimating the relationship between energy demanded and energy price, income, and substitute price. The data used in this study include observations from each of the 48 continental U.S. states over the period 1970 through Data on total energy consumption (measured in billions of BTU s) for coal, electricity, natural gas, and fuel oil along with their corresponding average energy price level (measured in MMBTU s) and crude oil prices (measured in dollars per barrel) were collected by the Department of Energy and Independence from the U.S. Energy Information Administration. Furthermore, energy consumption and price were collected for each sector in the economy including residential, commercial, and industrial. Additional data on personal income, nominal gross domestic product, gross state product, consumer price index, and population were collected from the Bureau of Economic Analysis. 2 Finally, data used to calculate the climate index were collected from the National Oceanic and Atmospheric Administration, National Climate Data Center. 3 All variables are measured in natural logs so that the results can be interpreted in terms of elasticities. There is growing concern over the emissions of greenhouse gases in the United States that has provoked federal policies complementary to reducing energy demand and encouraging the use of more efficient energy technologies. These policies will have an effect on the cost of energy and thus a quantitative examination of the energy demand is warranted. The purpose of this section is to estimate the demand for energy in order to examine the sensitivity of energy consumption to changes in price and income. The model used to estimate the demand curve is a stock-flow model developed by Houthakker et al. (1974) and used by Berstein and Griffin (2005). 4 This model is conducive to short-run dynamics in the demand relationship and allows for computation of both short-run and long-run relationships. Specifically, this setup gives consumers the ability to modify their energy consumption in the short run by adjusting their use of energy-intensive appliances (the flow). In the long-run, consumers have access to more energy-efficient technologies and can adjust the type of appliances (the stock) they use much more easily. In estimating the demand curve and corresponding elasticities, energy price and quantity are determined by interaction of supply and demand. Therefore, in order to isolate the effects on the demand curve, the following assumptions need to hold: (1) the model includes all the determinants of the demand for energy; (2) energy price is exogenous; and (3) there is no serial correlation in the residuals. These assumptions allow identification of the model parameters through shifts in the supply curve that, subsequently, trace out the demand curve. In reference to assumption (1), and consistent 1 We exclude Hawaii and Alaska given their unique climate and energy use. 2 The Consumer Price Index (CPI) was used to convert prices to 2010 dollars. 3 Climate index was calculated as the sum of the number of heating degree days and cooling degree days. 4 See the Appendix for more technical details of estimation. 13

19 with microeconomic theory, the model captures the basic attributes of the demand curve such as income and substitute price. 5 Furthermore, we control for changes in energy demand relating to climate and population. 6 With respect to the second assumption, as argued by Bernstein and Griffin (2005), a component of utility bills are derived from fuel costs which are determined on a world market and therefore not affected by changes in demand from the U.S. Table 1: National Estimates: Demand for Energy (1) (2) (3) (4) Variables Coal Electricity Natural Gas Fuel Oil Short-Run Elasticity Estimates Price *** *** *** (0.179) (0.0360) (0.115) (0.127) Substitute Price (0.125) (0.0139) (0.0404) (0.0274) Income *** * (0.292) (0.0438) (0.0734) (0.240) Long-Run Elasticity Estimates Price *** *** *** (0.387) (0.0819) (0.507) (0.314) Substitute Price * *** (0.686) (0.0474) (0.281 (0.0998) Income *** 1.111*** 0.651*** (0.585) (0.0617) (0.281) (0.175) Observations 1,860 1,872 1,872 1,872 R-squared Number of States Adj. R-Squared Log Likelihood Notes: Robust standard errors in parentheses. Asterisks denote significance at the following levels: *** p<0.01, ** p<0.05, * p<0.1. The law of demand dictates that the price elasticity of demand should be negative, suggesting that quantity demanded for energy decreases with increases in the price, all else equal. Furthermore, provided that energy is a normal good, income elasticity of 5 For our model, natural gas serves as a substitute for electricity, fuel oil, and coal. Electricity serves as a substitute for natural gas. 6 In addition to the control variables, we control for state-specific fixed effects such as geography and other state-specific policies that may influence energy demand. Also, time fixed effects are included to control for time-varying factors that affect all states over time (e.g. Federal policies affecting all states or business cycle fluctuations). 14

20 demand is expected to be positive; accordingly, increases in income result in increases in energy consumption, all else equal. Finally, cross-price elasticity is expected to be positive if electricity and natural gas are substitutes and negative if they are complements. Long-run price elasticities are expected to be higher (in absolute values) as consumers have greater flexibility in adopting more energy-efficient technologies and avoiding price increases. Likewise, income elasticity is expected to be greater in the long run than in the short run. Using a panel of U.S. states, allowing for both state-specific fixed effects and time effects, estimates for energy demand are given in Table 1. Notice that all price elasticity estimates are negative as expected. In the short run, coal appears to be the most sensitive to price changes followed by natural gas consumption. For example, a 1% increase in price results in a 0.64% drop in coal consumption and a 0.42% drop in natural gas consumption. Electricity, on the other hand, has a price elasticity of only 0.20%, which is substantially below both coal and natural gas. This estimate is in line with the price elasticity found by Dahl and Roman (2004). This is an indication that there are no readily-available substitutes in the short run for electricity as compared to coal and natural gas. The price elasticity of fuel oil is insignificantly different from zero; therefore price does not affect the consumption of fuel oil. 7 With respect to income elasticity, only electricity and fuel oil display statistically significant income effects. For instance, a 1% increase in income leads to an increase in electricity consumption and fuel oil consumption of 0.13% and 0.43%, respectively. This result is as expected since both electricity and fuel oil are perceived as normal goods, thus increases in income are associated with more use of electricity-intensive appliances (e.g. consumers are more likely to leave lights and other appliances on when away from the home). Surprisingly, the estimate for the cross-price elasticity is not significant in any equation. This result implies that either natural gas is a bad proxy for energy substitutes or that substitution from natural gas to other energy sources might not be feasible in the short run. The long-run estimates in Table 1 show interesting results. For instance, natural gas is more sensitive to price changes (-1.71) compared to coal (-1.31). However, both goods are highly elastic in the long run since a 1% increase in price results in a 1.71% and 1.31% drop in consumption of natural gas and coal, respectively. Electricity maintains its position as being relatively inelastic compared to natural gas and coal but has higher price elasticity in the long run as expected. However, this estimate is significantly higher than the long-run price elasticity found by Dahl and Roman (2004). These results are consistent with the fact that consumers of energy have greater flexibility in avoiding price changes in the long run by adopting less energy-intensive appliances. Income effects appear to be more significant in the long run. Electricity, natural gas and fuel oil all have positive income effects. This finding makes sense that, all else equal, increases in income allow consumers to afford more energy-intensive appliances, thus increasing their energy consumption. Surprisingly, natural gas consumption has a high income elasticity. For example a 1% increase income in the long run results in an increase in natural gas consumption of 1.11%. 7 The possible reason for no price effects in the short run could be due to the storable characteristic of fuel oil in which consumers can avoid paying higher prices for a period of time (Kilian, 2008). 15

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